The Determinants of Work-Life Balance among Nurses in Public Hospital in Klang Valley

The need to have a healthy work-life balance is generally recognized across several professional domains. The present investigation was carried out within a public health institution situated in the Klang Valley region. The objective of this study is to investigate the impact of workload, time management, and work environment on the work-life balance of nurses, who serve as frontline healthcare professionals. The research utilized purposive sampling as one of the quantitative research methods. Online distribution was utilized as the means of data collection.  A grand total of 276 questionnaires were appropriately completed and afterward returned. The findings indicate that workload negatively impacts employee work-life balance when compared to aspects such as time management and work environment. All the variables found have a significant impact on employees  work-life balance in different positive and negative relationships

Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network

Bagworm is the most important insect defoliator of oil palm. The bagworm larvae scrape off the leaflets’ epidermis while the older larvae chew the leaflets and leaving multiple holes and causes the palm to lose its photosynthetic capability. A bagworm census should be carried out quickly to determine the extent of damage. However, the conventional practices are heavily dependent on in-situ data collection, which is destructive, less efficient, laborious, and costly. Recently, many studies have incorporated machine learning analysis such as artificial neural network (ANN) in agricultural fields especially in the development of pest prediction model. Therefore, this study was conducted to develop a weather-based bagworm prediction model using ANN-Feature Selection method. Bagworm censuses were done by identifying Metisa plana’s larval stage 1 (L1) to 7 (L7) from 13 random palms by cutting off frond number 17 biweekly and weather data was recorded by installing weather station in an oil palm plantation belongs to TH Plantation Berhad in Muadzam Shah, Pahang, Malaysia from July 2016 to June 2017. The results revealed that the significant weather parameters were frequent at time-lag 12. All the larval stage prediction models from ANN-Feature Selection were able to produce satisfactory R2 values ranging from 0.526 to 0.995. The best model was the L1 model with R2 value of 0.985 and the accuracy of more than 90 %

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network

Bagworm is the most important insect defoliator of oil palm. The bagworm larvae scrape off the leaflets’ epidermis while the older larvae chew the leaflets and leaving multiple holes and causes the palm to lose its photosynthetic capability. A bagworm census should be carried out quickly to determine the extent of damage. However, the conventional practices are heavily dependent on in-situ data collection, which is destructive, less efficient, laborious, and costly. Recently, many studies have incorporated machine learning analysis such as artificial neural network (ANN) in agricultural fields especially in the development of pest prediction model. Therefore, this study was conducted to develop a weather-based bagworm prediction model using ANN-Feature Selection method. Bagworm censuses were done by identifying Metisa plana’s larval stage 1 (L1) to 7 (L7) from 13 random palms by cutting off frond number 17 biweekly and weather data was recorded by installing weather station in an oil palm plantation belongs to TH Plantation Berhad in Muadzam Shah, Pahang, Malaysia from July 2016 to June 2017. The results revealed that the significant weather parameters were frequent at time-lag 12. All the larval stage prediction models from ANN-Feature Selection were able to produce satisfactory R2 values ranging from 0.526 to 0.995. The best model was the L1 model with R2 value of 0.985 and the accuracy of more than 90%

Relationship between Metisa plana and its parasitoids (braconidae and ichneumonidae) at different time-lags in oil palm plantation

Bagworm or Metisa plana, is the most important defoliator insect in the oil palm cultivation, while parasitoids such as Braconidae and Ichneumonidae are the common main natural enemies for bagworm. Although the relationship between natural enemies with its insect pest has been broadly investigated, little is dedicated for assessing the delayed effects of the bagworm population on parasitoids. In this study, the abundance relationship between bagworm and its common parasitoids (Braconidae and Ichneumonidae) was assessed at different time-lags under field condition. Bagworm censuses for instar stages first (L1) to seven (L7) were conducted biweekly in an oil palm plantation belongs to TH Plantation Berhad in Muadzam Shah, Pahang from July 2016 to July 2017, along with bagworm’s parasitoid from Braconidae and Ichneumonidae family. The results revealed that Braconidae presence was associated positively with bagworm at week 6 to 12, whereby the associations were stronger for late instar stages. On the other hand, Ichneumonidae presence was positively related to middle instar stages at early time-lag i.e. week 2 to 6. In other words, Ichneumonidae population is expected to increase after 2 to 6 weeks given increasing population of bagworm, while Braconidae population is likely to rise after 6 to 12 weeks of the increasing population of bagworm

Big Data Maturity Assessment Models: A Systematic Literature Review

Big Data and analytics have become essential factors in managing the COVID-19 pandemic. As no company can escape the effects of the pandemic, mature Big Data and analytics practices are essential for successful decision-making insights and keeping pace with a changing and unpredictable marketplace. The ability to be successful in Big Data projects is related to the organization’s maturity level. The maturity model is a tool that could be applied to assess the maturity level across specific key dimensions, where the maturity levels indicate an organization’s current capabilities and the desirable state. Big Data maturity models (BDMMs) are a new trend with limited publications published as white papers and web materials by practitioners. While most of the related literature might not have covered all of the existing BDMMs, this systematic literature review (SLR) aims to contribute to the body of knowledge and address the limitations in the existing literature about the existing BDMMs, assessment dimensions, and tools. The SLR strategy in this paper was conducted based on guidelines to perform SLR in software engineering by answering three research questions: (1) What are the existing maturity assessment models for Big Data? (2) What are the assessment dimensions for Big Data maturity models? and (3) What are the assessment tools for Big Data maturity models? This SLR covers the available BDMMs written in English and developed by academics and practitioners (2007–2022). By applying a descriptive qualitative content analysis method for the reviewed publications, this SLR identified 15 BDMMs (10 BDMMs by practitioners and 5 BDMMs by academics). Additionally, this paper presents the limitations of existing BDMMs. The findings of this paper could be used as a grounded reference for assessing the maturity of Big Data. Moreover, this paper will provide managers with critical insights to select the BDMM that fits within their organization to support their data-driven decisions. Future work will investigate the Big Data maturity assessment dimensions towards developing a new Big Data maturity model

Explore Big Data Analytics Applications and Opportunities: A Review

Big data applications and analytics are vital in proposing ultimate strategic decisions. The existing literature emphasizes that big data applications and analytics can empower those who apply Big Data Analytics during the COVID-19 pandemic. This paper reviews the existing literature specializing in big data applications pre and peri-COVID-19. A comparison between Pre and Peri of the pandemic for using Big Data applications is presented. The comparison is expanded to four highly recognized industry fields: Healthcare, Education, Transportation, and Banking. A discussion on the effectiveness of the four major types of data analytics across the mentioned industries is highlighted. Hence, this paper provides an illustrative description of the importance of big data applications in the era of COVID-19, as well as aligning the applications to their relevant big data analytics models. This review paper concludes that applying the ultimate big data applications and their associated data analytics models can harness the significant limitations faced by organizations during one of the most fateful pandemics worldwide. Future work will conduct a systematic literature review and a comparative analysis of the existing Big Data Systems and models. Moreover, future work will investigate the critical challenges of Big Data Analytics and applications during the COVID-19 pandemic